class_docstr: Lazy loading, memoizing, instantiation of SparkSessions.
  
  Useful when you want to defer SparkSession instantiation and provide access to the same
  instance throughout the application.
class_docstr_tok: ["Lazy","loading",",","memoizing",",","instantiation","of","SparkSessions",".","Useful","when","you","want","to","defer","SparkSession","instantiation","and","provide","access","to","the","same","instance","throughout","the","application","."]
docstr: JVM SparkContext Hadoop configurations.
docstr_tok: ["JVM","SparkContext","Hadoop","configurations","."]
code: def _get_jsc_hadoop_configs(self) -> List[Tuple[str, str]]:
      """JVM SparkContext Hadoop configurations."""
      # Skip generation of _SUCCESS files to speed up writes.
      return [
          ("mapreduce.fileoutputcommitter.marksuccessfuljobs", "false")
      ]
  
file_path: sagemaker-python-sdk/src/sagemaker/feature_store/feature_processor/_spark_factory.py
imports: from __future__ import absolute_import
  from functools import lru_cache
  from typing import List, Tuple, Dict
  import feature_store_pyspark
  import feature_store_pyspark.FeatureStoreManager
  from pyspark.conf import SparkConf
  from pyspark.context import SparkContext
  from pyspark.sql import SparkSession
  from sagemaker.feature_store.feature_processor._env import EnvironmentHelper
label_desc: Use image node_id 4 for calling the SparkSessionFactory obj's underlying member method code with example usage: obj._get_jsc_hadoop_configs() and returns: list